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Big Data News & Analysis

If you're interested in topics like big data analytics, business intelligence, business analytics and data warehousing, then you have a friend in Joanna Schloss. In her role as subject matter expert in the Dell Center of Excellence, Joanna helps clients deal with the challenges of data and information management, including such things as managing multiple platforms, applications systems and analytic environments. We're fortunate to have her share her expertise on CMSWire, where she regularly engages our readers with her thought-provoking posts.

Forrester analyst Mike Gualtieri made it official last month when he declared that the economic benefits of Hadoop will make adoption essential, not optional, for enterprises as they move into 2015.

And putting dollars aside, big data analytics are quickly becoming part of everyone’s job, says Tom Davenport, a Distinguished Professor of Information Technology and Management at Babson College and author of Big Data at Work. He asserts that we are entering the Analytics 3.0 era, in which insights can be delivered anytime you need them via any device.

Advanced analytics is becoming an enterprise essential. But most companies appear content to stick with the benefits of the "small data." the technology generates rather than make the necessary investments to capitalize on big data.

That's true even though a majority agree that big data is important, according to a new study commissioned by Dell and the International Institute for Analytics.

The study defines advanced analytics as predictive and prescriptive analytics rather than simple reporting.

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Teradata is serious about big data. In fact, when it comes to Hadoop, it wants to be the one-stop shop for its large customer base. Aside from making its existing products more powerful and more capable, it recently strengthened its support partnerships with independent Hadoop distro providers MapR and Cloudera, and signaled a continued commitment to Hortonworks, which in now publicly traded as Nasdaq:HDP.

Today the company announced that it has purchase RainStor, a provider of patented Enterprise archiving solutions around Hadoop.

Many years ago, a friend who is a partner at McKinsey related a story from his years in grad school. He was at an AT&T presentation on Unix, held in a room full of bearded, jean-clad engineers and coders to hear about the wonders of Unix. He was standing in the back of the room in a jacket and tie, next to the other few coat-and-tie-attired attendees, who my friend characterized as IT managers from Fortune 100 companies.

During a break in the talk, one of the F100 folks leaned to another and whispered “I like everything I’ve seen about Unix -- except the people in this room.”

Jump ahead 30 years. Have you ever been to an ApacheCon? It’s like living in the 70s all over again.

Some people believe it takes two full years for students to fully understand and master new concepts. The first year in the cycle, when a new concept is introduced, is considered a learning year. The following year is considered a growth and review year.

The thinking holds that while students technically learn about new concepts during the first year, it’s not until the second year that they can truly begin applying them in an active manner, one that displays measurable growth and development.

In many ways, the landscape of big data at the close of 2014 can be described in similar terms.

Generally speaking, 2014 was a learning year. IT decision makers across all verticals realized they could no longer ignore the changing landscape brought on by growth in the volume, velocity and variety of data. Investments were made and infrastructure was overhauled. After many years of pomp and circumstance, 2014 was the year big data finally become the infrastructure of reality.

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IBM has patented two techniques that use analytics to improve cloud performance and efficiency in the data center. These patents probably won’t be put into practical use for at least a few years, but their development is noteworthy. Essentially, they are part of IBM's plan to stay on top of data center performance demands and needs as the Internet of Things gets into full swing.

Whether you realize it or not, every day you work on what International Data Corporation (IDC) describes as the third platform. By the end of next year, you will be doing a lot more work on it and using its technologies.

The rise and expansion of the third platform has reached a tipping point according to IDC, and with the growth of investments in the Internet of Things, big data, data centers, mobile and social computing, there is, in effect, no way of avoiding it.

IBM just announced a new use for Watson, its supercomputer: a freemium analytics service that's available to all business users. It provides business professionals a unified experience and natural language dialogue so they can make better use data to achieve business goals, IBM claims.

Since it announced a very limited beta in September, IBM claims 22,000 people have signed up for the service. That seems way below the minimum threshold needed to make this a viable business, which may explain why the company is now opening it up to everyone. The freemium service will automate the once time-consuming tasks of data preparation, predictive analysis and visual storytelling for business professionals.

Less than a month after Hortonworks unveiled the news that it confidentially filed its S-1 with the US Securities and Exchange Commission, it has set its expected IPO date for Dec. 12, according to Nasdaq. The offer amount is $96.6 million, with prices for individual shares set at $12 to $14. A total of six million shares will be made available.

Hortonworks is the first independent, commercial Hadoop provider to go public. Some believe that its IPO will be a bellwether for the $50.2 billion data-related segments (data management and data storage) of the enterprise software market.

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We saw the usual spate of announcements coming from the major software companies this fall. Lots of new extensions to the Salesforce.com and Oracle marketing clouds, Adobe extensions and updates to Creative Cloud, and most recently, IBM’s Launch of IBM Verse. And that’s just the big players.

All of the fall software announcements had one thing in common: analytics. Whether it’s sales analysis, data for making marketing decisions or prioritizing emails, analytics -- predictive analytics especially -- is everywhere. It’s behind the latest supply chain management tools and integrated into CRM systems. Analytics is in email too.

If anyone has the scoop on what people are thankful for, it’s Facebook. They don’t need to tap anyone on the shoulder, send out any surveys or buy any data sets. The world’s leading social networking site is rich in “likes," data, and some of the world’s best data scientists.

And not just that, but Facebook also has enthusiastic members who thrive on holiday challenges like “write down 3 things you are thankful for every day for the next 5 days.”

To a data scientist an onslaught of information like this spells opportunity to discover what most of us, collectively, believe is good in our lives and in the world.

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